Causal Interpretation

Causal interpretation aims to understand and model cause-and-effect relationships within data, moving beyond simple correlations to reveal underlying mechanisms. Current research focuses on developing and evaluating methods for causal inference in various domains, including natural language processing (using large language models and counterfactual reasoning), machine learning (leveraging techniques like disentanglement and structural equation models), and even social sciences (analyzing social media data to understand behavior). This work is significant because establishing causality allows for more robust predictions, improved model explainability, and better-informed decision-making in diverse fields like healthcare, AI safety, and social sciences.

Papers

September 28, 2022

Revisiting Few-Shot Learning from a Causal Perspective
Guoliang Lin, Yongheng Xu, Hanjiang Lai, Jian Yin
LeArning Abstract Shot Learning Causal Interpretation Metric Based Few Shot

July 11, 2022

CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts
Muskan Garg, Chandni Saxena, Veena Krishnan, Ruchi Joshi, Sriparna Saha, Vijay Mago, Bonnie J Dorr
Social Medium Mental Health Causal Analysis Annotated Corpus Causal Interpretation Interpretable Causal Causal Categorization Meteor Detection

May 15, 2022

Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning
Yikuan Li, Mohammad Mamouei, Shishir Rao, Abdelaali Hassaine, Dexter Canoy, Thomas Lukasiewicz, Kazem Rahimi, Gholamreza Salimi-Khorshidi
Counterfactual Explanation Concept Bottleneck Model Counterfactual Reasoning Causal Interpretation Representation Learning Framework Clinical Outcome Prediction

March 3, 2022

Local Constraint-Based Causal Discovery under Selection Bias
Philip Versteeg, Cheng Zhang, Joris M. Mooij
Causal Relation Selection Bias Causal Interpretation Constraint Based Causal Discovery

January 15, 2022

Automated causal inference in application to randomized controlled clinical trials
Jiqing Wu, Nanda Horeweg, Marco de Bruyn, Remi A. Nout, Ina M. Jürgenliemk-Schulz, Ludy C. H. W. Lutgens, Jan J. Jobsen, Elzbieta M. van der Steen-Banasik, Hans W. Nijman, Vincent T. H. B. M. Smit, Tjalling Bosse, Carien L. Creutzberg, Viktor H. Koelzer
Application Proficiency Causal Inference Clinical Trial Causal Variable Causal Interpretation

Causal Interpretation

Papers

Revisiting Few-Shot Learning from a Causal Perspective

CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts

Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning

Local Constraint-Based Causal Discovery under Selection Bias

Automated causal inference in application to randomized controlled clinical trials